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An MT-InSAR Data Partition Strategy for Sentinel-1A/B TOPS Data

Authors :
Yuexin Wang
Guangcai Feng
Zhixiong Feng
Yuedong Wang
Xiuhua Wang
Shuran Luo
Yinggang Zhao
Hao Lu
Source :
Remote Sensing, Vol 14, Iss 18, p 4562 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

The Sentinel-1A/B satellite launched by European Space Agency (ESA) in 2014 provides a huge amount of free Terrain Observation by Progressive Scans (TOPS) data with global coverage to the public. The TOPS data have a frame width of 250 km and have been widely used in surface deformation monitoring. However, traditional Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) methods require large computer memory and time when processing full resolution data with large width and long strips. In addition, they hardly correct atmospheric delays and orbital errors accurately over a large area. In order to solve these problems, this study proposes a data partition strategy based on MT-InSAR methods. We first process the partitioned images over a large area by traditional MT-InSAR method, then stitch the deformation results into a complete deformation result by correcting the offsets of adjacent partitioned images. This strategy is validated in a flat urban area (Changzhou City in Jiangsu province, China), and a mountainous region (Qijiang in Chongqing City, China). Compared with traditional MT-InSAR methods, the precision of the results obtained by the new strategy is improved by about 5% for Changzhou city and about 15% for Qijiang because of its advantage in atmospheric delay correction. Furthermore, the proposed strategy needs much less memory and time than traditional methods. The total time needed by the traditional method is about 20 h, and by the proposed method, is about 8.7 h, when the number of parallel processing is 5 in the Changzhou city case. The time will be further reduced when the number of parallel processes increases.

Details

Language :
English
ISSN :
20724292
Volume :
14
Issue :
18
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.90312349c8124cb18c4e79c07c4c99fd
Document Type :
article
Full Text :
https://doi.org/10.3390/rs14184562